ACOUSTIC SOURCE TRACKING AND SELECTION

• US 20160071526A1
• Filed: 09/08/2015
• Published: 03/10/2016
• Est. Priority Date: 09/09/2014
• Status: Abandoned Application

• Current Assignee: Analog Devices, Inc.
• Original Assignee: Analog Devices, Inc.
• Inventors: WINGATE, DAVID, STEIN, NOAH DANIEL, VIGODA, BENJAMIN, OHIOMOBA, PATRICK, DONNELLY, BRIAN
• Application Number:
  US14/847,818
• Publication Number:
  US 20160071526A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01S 3/802 Systems for determining direction or deviation from predetermined direction
• CPC Class Codes: G01S 3/807 the desired condition being maintained automatically
• CPC Class Codes: G10L 21/028 using properties of sound source

Abstract

The present disclosure relates generally to improving acoustic source tracking and selection and, more particularly, to techniques for acoustic source tracking and selection using motion or position information. Embodiments of the present disclosure include systems designed to select and track acoustic sources. In one embodiment, the system may be realized as an integrated circuit including a microphone array, motion sensing circuitry, position sensing circuitry, analog-to-digital converter (ADC) circuitry configured to convert analog audio signals from the microphone array into digital audio signals for further processing, and a digital signal processor (DSP) or other circuitry for processing the digital audio signals based on motion data and other sensor data. Sensor data may be correlated to the analog or digital audio signals to improve source separation or other audio processing.

Claims

1. A system for processing at least one signal acquired using one or more acoustic sensors, the at least one signal having contributions from one or more acoustic sources, the system comprising:

a memory configured to store computer executable instructions; and

a processor communicatively connected to or comprising the memory and configured, when executing the instructions, to;

obtain sensor data from one or more sensors other than the one or more acoustic sensors; and

use the sensor data in executing an acoustic source separation algorithm on the at least one acquired signal to separate from the at least one acquired signal one or more contributions from a predetermined acoustic source of the one or more acoustic sources.

2. The system according to claim 1, wherein the acoustic source separation algorithm comprises:

computing time-dependent spectral characteristics from the at least one acquired signal, the spectral characteristics comprising a plurality of components;

computing direction estimates from at least two signals acquired using one or more acoustic sensors, each component of a first subset of the plurality of components having a corresponding one or more of the direction estimates; and

performing iterations of a nonnegative tensor factorization (NTF) model for the one or more acoustic sources, the iterations comprising (a) combining values of a plurality of parameters of the NTF model with the computed direction estimates to separate from the acquired signals one or more contributions from the predetermined acoustic source.

3. The system according to claim 1, wherein the acoustic source separation algorithm comprises:

computing time-dependent spectral characteristics from the at least one acquired signal, the spectral characteristics comprising a plurality of components;

applying a first model to the time-dependent spectral characteristics, the first model configured to compute property estimates of a property, each component of a first subset of the components having a corresponding one or more property estimates of the property; and

performing iterations of a nonnegative tensor factorization (NTF) model for the one or more acoustic sources, the iterations comprising (a) combining values of a plurality of parameters of the NTF model with the computed property estimates to separate from the at least one acquired signal one or more contributions from the predetermined acoustic source.

4. The system according to claim 1, wherein the acoustic source separation algorithm comprises:

computing time-dependent spectral characteristics from the at least one acquired signal, the spectral characteristics comprising a plurality of components;

accessing at least a first model configured to predict contributions from the predetermined acoustic source of the one or more acoustic sources; and

performing iterations of a nonnegative tensor factorization (NTF) model for the one or more acoustic sources, the iterations comprising running the first model to separate from the at least one acquired signal one or more contributions from the predetermined acoustic source.

5. The system according to claim 1, wherein the acoustic source separation algorithm comprises:

computing time-dependent spectral characteristics from the at least one acquired signal, the spectral characteristics comprising a plurality of components;

computing direction estimates from at least two signals of one or more signals acquired using the one or more acoustic sensors, each computed component of the spectral characteristics having a corresponding one of the direction estimates;

performing a decomposition procedure using the computed spectral characteristics and the computed direction estimates as input to identify a plurality of sources of the plurality of signals, each component of the spectral characteristics having a computed degree of association with at least one of the identified sources and each source having a computed degree of association with at least one direction estimate; and

using a result of the decomposition procedure to selectively process a signal from one of the sources.

6. The system according to claim 1, wherein the acoustic source separation algorithm comprises:

accessing an indication of a current block size, the current block size defining a size of a portion of the at least one acquired signal to be analyzed to separate from the at least one acquired signal one or more contributions from the predetermined acoustic source of the one or more acoustic sources;

analyzing a first portion of the at least one acquired signal, the first portion being of the current block size, by;

computing one or more first characteristics from data of the first portion, and

using the computed one or more first characteristics, or derivatives thereof, in performing iterations of a nonnegative tensor factorization (NTF) model for the one or more acoustic sources for the data of the first portion to separate, from at least the first portion of the at least one acquired signal, one or more first contributions from the predetermined acoustic source; and

analyzing a second portion of the at least one acquired signal, the second portion being of the current block size and being temporaly shifted with respect to the first portion, by;

computing one or more second characteristics from data of the second portion, and

using the computed one or more second characteristics, or derivatives thereof, in performing iterations of the NTF model for the data of the second portion to separate, from at least the second portion of the at least one acquired signal, one or more second contributions from the predetermined acoustic source.

7. The system according to claim 1, wherein using the sensor data comprises correlating the sensor data to the at least one acquired signal.

8. The system according to claim 1, wherein the sensor data comprises data indicative of occurrence of an event or/and change of a state of a surrounding where the at least one signal is acquired.

9. The system according to claim 8, wherein using the sensor data comprises:

identifying a time instance or a time period of the at least one acquired signal corresponding to a time instance or a time period when the event occurred or the state of the surrounding changed, and

adjusting the acoustic source separation algorithm based on the identified time instance or the time period.

10. The system according to claim 9, wherein adjusting the acoustic source separation algorithm based on the identified time instance or the time period comprises adjusting the acoustic source separation algorithm to account for the occurrence of the event or/and the change of the state of the surrounding.

11. The system according to claim 9, wherein adjusting the acoustic source separation algorithm based on the identified time instance or the time period comprises adjusting noise reduction algorithm to account for the occurrence of the event or/and the change of the state of the surrounding.

12. The system according to claim 1, wherein the processor is further configured to:

determine a location and/or an orientation of the one or more acoustic sensors; and

further use the determined location and/or orientation of the one or more acoustic sensors in executing the acoustic source separation algorithm.

13. The system according to claim 12, wherein the processor is configured to determine the location and/or the orientation of the one or more acoustic sensors based on the sensor data.

14. One or more non-transitory computer readable storage media encoded with software for processing at least one signal acquired using one or more acoustic sensors, the at least one signal having contributions from one or more acoustic sources, the software comprising computer executable instructions configured, when executed, to:

obtain sensor data from one or more sensors other than the one or more acoustic sensors; and

use the sensor data in executing an acoustic source separation algorithm on the at least one acquired signal to separate from the at least one acquired signal one or more contributions from a predetermined acoustic source of the one or more acoustic sources.

15. The one or more non-transitory computer readable storage media according to claim 14, wherein using the sensor data comprises correlating the sensor data to the at least one acquired signal.

16. The one or more non-transitory computer readable storage media according to claim 14, wherein the sensor data comprises data indicative of occurrence of an event or/and change of a state of a surrounding where the at least one signal is acquired.

17. The one or more non-transitory computer readable storage media according to claim 16, wherein using the sensor data comprises:

identifying a time instance or a time period of the at least one acquired signal corresponding to a time instance or a time period when the event occurred or the state of the surrounding changed, and

adjusting the acoustic source separation algorithm based on the identified time instance or the time period.

18. The one or more non-transitory computer readable storage media according to claim 17, wherein adjusting the acoustic source separation algorithm based on the identified time instance or the time period comprises adjusting the acoustic source separation algorithm or/and a noise reduction algorithm to account for the occurrence of the event or/and the change of the state of the surrounding.

19. The one or more non-transitory computer readable storage media according to claim 14, wherein the software further comprises computer executable instructions configured, when executed, to:

determine a location and/or an orientation of the one or more acoustic sensors; and

further use the determined location and/or orientation of the one or more acoustic sensors in executing the acoustic source separation algorithm.

20. A method for processing at least one signal acquired using one or more acoustic sensors, the at least one signal having contributions from one or more acoustic sources, the method comprising:

obtaining sensor data from one or more sensors other than the one or more acoustic sensors; and

using the sensor data in executing an acoustic source separation algorithm on the at least one acquired signal to separate from the at least one acquired signal one or more contributions from a predetermined acoustic source of the one or more acoustic sources.